设计和开发通过烟方法和机器学习技术检测伪造系统
Urvashi Agrawal1, Narendra Bawane2, Najah Alsubaie3
1Department of Electronics and Telecommunication Engineering, Jhulelal Institute of Technology, Nagpur, India. urvashi.agrawal2000@gmail.com.
Scientific reports
|October 25, 2024
概括
这项研究引入了使用折射率和电子传感器检测食用油改的新方法. 机器学习算法在识别杂质方面实现了100%的准确性,确保了食品质量.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 传感器技术 传感器技术
背景情况:
- 造食用油对公众健康和经济完整性构成重大威胁.
- 准确和快速的检测方法对于确保食品安全和质量至关重要.
- 目前用于检测食用油改的方法可能耗时,需要专门的设备.
研究的目的:
- 开发和验证用于检测食用油中改的新方法.
- 调查折射率和基于电子传感器的杂质检测技术的有效性.
- 为了比较各种机器学习算法在识别食用油改方面的性能.
主要方法:
- 利用来自高级ATR-MIR光谱学和折射率测量的光谱数据进行基于数据集的分析.
- 开发了一种烟雾化技术,将实时硬件与MEMS和多通道气体传感器集成在一起.
- 使用机器学习算法,包括KNN,随机森林,CATBOOST和XGBOOST进行分类.
主要成果:
- 折射率变化有效地表明了与植物油 (较低的指数) 或动物脂肪 (较高的指数) 的改.
- 烟雾化方法成功提取了挥发性化合物,传感器导电率的变化与污染水平相关.
- 随机森林和XGBOOST算法表现出卓越的性能,在改检测中达到100%的准确性.
结论:
- 提出的方法为识别食用油改提供了可靠和准确的方法.
- 折射率,电子传感器和机器学习的整合为食品质量控制提供了强大的工具.
- 这项研究有助于确保消费者获得高质量的食用食品.
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